Search Results for author: Keqin Li

Found 23 papers, 2 papers with code

Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

no code implementations1 Mar 2024 Zeju Cai, Jianguo Chen, Yuting Fan, Zibin Zheng, Keqin Li

We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration.

Fairness Federated Learning

An Effective Index for Truss-based Community Search on Large Directed Graphs

no code implementations19 Jan 2024 Wei Ai, CanHao Xie, Tao Meng, Yinghao Wu, Keqin Li

Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks.

Community Detection Community Search +2

Fast Butterfly-Core Community Search For Large Labeled Graphs

no code implementations19 Jan 2024 JiaYi Du, Yinghao Wu, Wei Ai, Tao Meng, CanHao Xie, Keqin Li

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph.

Community Search

A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning

no code implementations3 Jan 2024 Wei Ai, FuChen Zhang, Tao Meng, Yuntao Shou, HongEn Shao, Keqin Li

To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).

Classification Contrastive Learning +2

Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition

no code implementations28 Dec 2023 Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities.

Contrastive Learning Graph Representation Learning +1

DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition

no code implementations17 Dec 2023 Wei Ai, Yuntao Shou, Tao Meng, Keqin Li

Specifically, we construct a weighted multi-relationship graph to simultaneously capture the dependencies between speakers and event relations in a dialogue.

Contrastive Learning Multimodal Emotion Recognition +1

Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations

no code implementations11 Dec 2023 Tao Meng, Yuntao Shou, Wei Ai, Nan Yin, Keqin Li

The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e. g., text, audio, image and video, which is a significant development direction for realizing machine intelligence.

Data Augmentation Generative Adversarial Network +2

A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning

no code implementations10 Dec 2023 Yuntao Shou, Tao Meng, Wei Ai, Nan Yin, Keqin Li

Unlike the traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is a more challenging problem that needs to deal with more complex emotional interaction relationships.

Emotion Recognition

CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation

no code implementations4 Dec 2023 Yuntao Shou, Wei Ai, Tao Meng, Keqin Li

Zero-shot age estimation aims to learn feature information about age from input images and make inferences about a given person's image or video frame without specific sample data.

Age Estimation Zero-Shot Learning

Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph

1 code implementation5 Jul 2022 Liang Li, Siwei Wang, Xinwang Liu, En Zhu, Li Shen, Kenli Li, Keqin Li

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.

Clustering

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.

Clustering Management

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.

Multi-Objective Reinforcement Learning

Stochastic Client Selection for Federated Learning with Volatile Clients

no code implementations17 Nov 2020 Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y. Zomaya

Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.

Fairness Federated Learning +1

An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

no code implementations3 Nov 2020 Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, Albert Y. Zomaya

The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.

Distributed Computing Fairness +1

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

no code implementations4 Jul 2020 Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.

Virology

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

no code implementations12 Apr 2019 Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, Philip S. Yu

We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.

Edge-computing

Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training

no code implementations21 Feb 2019 Chengjie Li, Ruixuan Li, Haozhao Wang, Yuhua Li, Pan Zhou, Song Guo, Keqin Li

Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.

Scheduling

A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization

1 code implementation30 Dec 2018 Pavlo Melnyk, Zhiqiang You, Keqin Li

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR).

Offline Handwritten Chinese Character Recognition

A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Huigui Rong, Kashif Bilal, Keqin Li, Philip S. Yu

In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.

Cloud Computing Data Compression +3

A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Kashif Bilal, Xu Zhou, Keqin Li, Philip S. Yu

In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.

Distributed Computing Scheduling

A novel graph structure for salient object detection based on divergence background and compact foreground

no code implementations30 Nov 2017 Chenxing Xia, Hanling Zhang, Keqin Li

Different from prior methods, we calculate the saliency value of each node based on the relationship between the corresponding node and the virtual node.

Object object-detection +3

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